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Yang L, Gu Y, Wang B, Sun M, Zhang L, Shi L, Wang Y, Zhang Z, Yin Y. A multivariable model of ultrasound and clinicopathological features for predicting axillary nodal burden of breast cancer: potential to prevent unnecessary axillary lymph node dissection. BMC Cancer 2023; 23:1264. [PMID: 38129804 PMCID: PMC10734063 DOI: 10.1186/s12885-023-11751-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 12/12/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND To develop a clinical model for predicting high axillary nodal burden in patients with early breast cancer by integrating ultrasound (US) and clinicopathological features. METHODS AND MATERIALS Patients with breast cancer who underwent preoperative US examination and breast surgery at the Affiliated Hospital of Nantong University (centre 1, n = 250) and at the Affiliated Hospital of Jiangsu University (centre 2, n = 97) between January 2012 and December 2016 and between January 2020 and March 2022, respectively, were deemed eligible for this study (n = 347). According to the number of lymph node (LN) metastasis based on pathology, patients were divided into two groups: limited nodal burden (0-2 metastatic LNs) and heavy nodal burden (≥ 3 metastatic LNs). In addition, US features combined with clinicopathological variables were compared between these two groups. Univariate and multivariate logistic regression analysis were conducted to identify the most valuable variables for predicting ≥ 3 LNs in breast cancer. A nomogram was then developed based on these independent factors. RESULTS Univariate logistic regression analysis revealed that the cortical thickness (p < 0.001), longitudinal to transverse ratio (p = 0.001), absence of hilum (p < 0.001), T stage (p = 0.002) and Ki-67 (p = 0.039) were significantly associated with heavy nodal burden. In the multivariate logistic regression analysis, cortical thickness (p = 0.001), absence of hilum (p = 0.042) and T stage (p = 0.012) were considered independent predictors of high-burden node. The area under curve (AUC) of the nomogram was 0.749. CONCLUSION Our model based on US variables and clinicopathological characteristics demonstrates that can help select patients with ≥ 3 LNs, which can in turn be helpful to predict high axillary nodal burden in early breast cancer patients and prevent unnecessary axillary lymph node dissection.
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Affiliation(s)
- Lei Yang
- Department of Medical Ultrasound, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226006, People's Republic of China
| | - Yifan Gu
- Department of Medical Ultrasound, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226006, People's Republic of China
| | - Bing Wang
- Department of Medical Ultrasound, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226006, People's Republic of China
| | - Ming Sun
- Department of Medical Ultrasound, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226006, People's Republic of China
| | - Lei Zhang
- Department of Medical Ultrasound, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226006, People's Republic of China
| | - Lei Shi
- Department of Medical Ultrasound, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226006, People's Republic of China
| | - Yanfei Wang
- Department of Medical Ultrasound, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226006, People's Republic of China
| | - Zheng Zhang
- Department of Medical Ultrasound, Affiliated Hospital of Jiangsu University, Zhenjiang, 212000, People's Republic of China.
| | - Yifei Yin
- Department of Medical Ultrasound, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, 226006, People's Republic of China.
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Li L, Zhao J, Zhang Y, Pan Z, Zhang J. Nomogram based on multiparametric analysis of early-stage breast cancer: Prediction of high burden metastatic axillary lymph nodes. Thorac Cancer 2023; 14:3465-3474. [PMID: 37916439 PMCID: PMC10719655 DOI: 10.1111/1759-7714.15139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 10/08/2023] [Accepted: 10/09/2023] [Indexed: 11/03/2023] Open
Abstract
BACKGROUND The Z0011 and AMAROS trials found that axillary lymph node dissection (ALND) was no longer mandatory for early-stage breast cancer patients who had one or two metastatic axillary lymph nodes (mALNs). The aim of our study was to establish a nomogram which could be used to quantitatively predict the individual likelihood of high burden mALN (≥3 mALN). METHODS We retrospectively analyzed 564 women with early breast cancer who had all undergone both ultrasound (US) and magnetic resonance imaging (MRI) to examine axillary lymph nodes before radical surgery. All the patients were divided into training (n = 452) and validation (n = 112) cohorts by computer-generated random numbers. Their clinicopathological features and preoperative imaging associated with high burden mALNs were evaluated by logistic regression analysis to develop a nomogram for predicting the probability of high burden mALNs. RESULTS Multivariate analysis showed that high burden mALNs were significantly associated with replaced hilum and the shortest diameter >10 mm on MRI, with cortex thickness >3 mm on US (p < 0.05 each). These imaging criteria plus higher grade (grades II and III) and quadrant of breast tumor were used to develop a nomogram calculating the probability of high burden mALNs. The AUC of the nomogram was 0.853 (95% CI: 0.790-0.908) for the training set and 0.783 (95% CI: 0.638-0.929) for the validation set. Both internal and external validation evaluated the accuracy of nomogram to be good. CONCLUSION A well-discriminated nomogram was developed to predict the high burden mALN in early-stage breast patients, which may assist the breast surgeon in choosing the appropriate surgical approach.
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Affiliation(s)
- Ling Li
- Department of Integrated Traditional and Western MedicineTianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for CancerTianjinChina
| | - Jing Zhao
- Department of Ultrasound Diagnosis and TreatmentTianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for CancerTianjinChina
| | - Yu Zhang
- Department of Breast ImagingTianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for CancerTianjinChina
| | - Zhanyu Pan
- Department of Integrated Traditional and Western MedicineTianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for CancerTianjinChina
| | - Jin Zhang
- The Third Department of Breast CancerTianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for CancerTianjinChina
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Kawaguchi S, Kinowaki K, Tamura N, Masumoto T, Nishikawa A, Shibata A, Tanaka K, Kobayashi Y, Ogura T, Sato J, Kawabata H. High-accuracy prediction of axillary lymph node metastasis in invasive lobular carcinoma using focal cortical thickening on magnetic resonance imaging. Breast Cancer 2023:10.1007/s12282-023-01457-2. [PMID: 37020090 PMCID: PMC10075493 DOI: 10.1007/s12282-023-01457-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Accepted: 04/02/2023] [Indexed: 04/07/2023]
Abstract
BACKGROUND Invasive lobular carcinoma (ILC) grows diffusely in a single-cell fashion, sometimes presenting only subtle changes in preoperative imaging; therefore, axillary lymph node (ALN) metastases of ILC are difficult to detect using magnetic resonance imaging (MRI). Preoperative underestimation of nodal burden occurs more frequently in ILC than in invasive ductal carcinoma (IDC), however, the morphological assessment for metastatic ALNs of ILC have not fully been investigated. We hypothesized that the high false-negative rate in ILC is caused by the discrepancy in the MRI findings of ALN metastases between ILC and IDC and aimed to identify the MRI finding with a strong correlation with ALN metastasis of ILC. METHOD This retrospective analysis included 120 female patients (mean ± standard deviation age, 57.2 ± 11.2 years) who underwent upfront surgery for ILC at a single center between April 2011 and June 2022. Of the 120 patients, 35 (29%) had ALN metastasis. Using logistic regression, we constructed prediction models based on MRI findings: primary tumor size, focal cortical thickening (FCT), cortical thickness, long-axis diameter (LAD), and loss of hilum (LOH). RESULTS The area under the curves were 0.917 (95% confidence interval [CI] 0.869-0.968), 0.827 (95% CI 0.758-0.896), 0.754 (95% CI 0.671-0.837), and 0.621 (95% CI 0.531-0.711) for the FCT, cortical thickness, LAD, and LOH models, respectively. CONCLUSIONS FCT may be the most relevant MRI finding for ALN metastasis of ILC, and although its prediction model may lead to less underestimation of the nodal burden, rigorous external validation is required.
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Affiliation(s)
- Shun Kawaguchi
- Department of Breast and Endocrinology Surgery, Toranomon Hospital, 2-2-2 Toranomon, Minato City, Tokyo, 105-8470, Japan.
| | | | - Nobuko Tamura
- Department of Breast and Endocrinology Surgery, Toranomon Hospital, 2-2-2 Toranomon, Minato City, Tokyo, 105-8470, Japan
| | - Tomohiko Masumoto
- Department of Diagnostic Radiology, Toranomon Hospital, Tokyo, Japan
| | - Aya Nishikawa
- Department of Breast and Endocrinology Surgery, Toranomon Hospital, 2-2-2 Toranomon, Minato City, Tokyo, 105-8470, Japan
| | - Akio Shibata
- Department of Breast and Endocrinology Surgery, Toranomon Hospital, 2-2-2 Toranomon, Minato City, Tokyo, 105-8470, Japan
| | - Kiyo Tanaka
- Department of Breast and Endocrinology Surgery, Toranomon Hospital, 2-2-2 Toranomon, Minato City, Tokyo, 105-8470, Japan
| | - Yoko Kobayashi
- Department of Breast and Endocrinology Surgery, Toranomon Hospital, 2-2-2 Toranomon, Minato City, Tokyo, 105-8470, Japan
| | - Takuya Ogura
- Department of Breast and Endocrinology Surgery, Toranomon Hospital, 2-2-2 Toranomon, Minato City, Tokyo, 105-8470, Japan
| | - Junichiro Sato
- Department of Pathology, Toranomon Hospital, Tokyo, Japan
| | - Hidetaka Kawabata
- Department of Breast and Endocrinology Surgery, Toranomon Hospital, 2-2-2 Toranomon, Minato City, Tokyo, 105-8470, Japan
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Kim H, Han BK, Ko EY, Ko ES, Choi JS. Magnetic resonance imaging evaluation of single axillary lymph node metastasis in breast cancer: Emphasis on the location of lymph nodes. Medicine (Baltimore) 2022; 101:e31836. [PMID: 36550794 PMCID: PMC9771340 DOI: 10.1097/md.0000000000031836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
To evaluate the frequency and location of abnormal lymph nodes (LNs) in breast cancer patients with a single axillary lymph node (ALN) metastasis on breast magnetic resonance imaging (MRI). We retrospectively reviewed the MRI findings of 219 consecutive patients with breast cancer with single ALN metastasis who were surgically confirmed at our institution between January 2018 and December 2018. The morphological features and locations of the abnormal LN on MRI were analyzed. Pathology reports were reviewed to evaluate the size of the metastases and whether they were sentinel LNs (SLNs). Of the 219 patients with a single ALN metastasis, 56 (25.6%) showed abnormal MRI findings. Of these, 54 (96.4%) had either the lowest or second-lowest LN in the level I axilla. In 184 (91.5%) of 201 patients who underwent SLN biopsy, the metastatic LN were SLN. Macrometastases were found more frequently in cases with abnormal LNs than in those with normal-looking LNs (P = .004). The most frequent morphological feature of metastatic ALNs was a diffuse cortical thickening of 3 to 5 mm (37.5%). Although MRI findings of single ALN metastasis in breast cancer patients are none or minimal, abnormalities are observed in the lowest or second-lowest LN in the lower axilla when present, suggesting the location of the SLNs.
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Affiliation(s)
- Haejung Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Boo-Kyung Han
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- * Correspondence: Boo-Kyung Han, Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul 06351, Korea (e-mail: )
| | - Eun Young Ko
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Eun Sook Ko
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Ji Soo Choi
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
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Xu Z, Ding Y, Zhao K, Han C, Shi Z, Cui Y, Liu C, Lin H, Pan X, Li P, Chen M, Wang H, Deng X, Liang C, Xie Y, Liu Z. MRI characteristics of breast edema for assessing axillary lymph node burden in early-stage breast cancer: a retrospective bicentric study. Eur Radiol 2022; 32:8213-8225. [PMID: 35704112 DOI: 10.1007/s00330-022-08896-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 04/17/2022] [Accepted: 05/19/2022] [Indexed: 02/05/2023]
Abstract
OBJECTIVES To investigate whether breast edema characteristics at preoperative T2-weighted imaging (T2WI) could help evaluate axillary lymph node (ALN) burden in patients with early-stage breast cancer. METHODS This retrospective study included women with clinical T1 and T2 stage breast cancer and preoperative MRI examination in two independent cohorts from May 2014 to December 2020. Low (< 3 LNs+) and high (≥ 3 LNs+) pathological ALN (pALN) burden were recorded as endpoint. Breast edema score (BES) was evaluated at T2WI. Univariable and multivariable analyses were performed by the logistic regression model. The added predictive value of BES was examined utilizing the area under the curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI). RESULTS A total of 1092 patients were included in this study. BES was identified as the independent predictor of pALN burden in primary (n = 677) and validation (n = 415) cohorts. The analysis using MRI-ALN status showed that BES significantly improved the predictive performance of pALN burden (AUC: 0.65 vs 0.71, p < 0.001; IDI = 0.045, p < 0.001; continuous NRI = 0.159, p = 0.050). These results were confirmed in the validation cohort (AUC: 0.64 vs 0.69, p = 0.009; IDI = 0.050, p < 0.001; continuous NRI = 0.213, p = 0.047). Furthermore, BES was positively correlated with biologically invasive clinicopathological factors (p < 0.05). CONCLUSIONS In individuals with early-stage breast cancer, preoperative MRI characteristics of breast edema could be a promising predictor for pALN burden, which may aid in treatment planning. KEY POINTS • In this retrospective study of 1092 patients with early-stage breast cancer from two cohorts, the MRI characteristic of breast edema has independent and additive predictive value for assessing axillary lymph node burden. • Breast edema characteristics at T2WI positively correlated with biologically invasive clinicopathological factors, which may be useful for preoperative diagnosis and treatment planning for individual patients with breast cancer.
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Affiliation(s)
- Zeyan Xu
- School of Medicine, South China University of Technology, Guangzhou, 510006, China
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China
| | - Yingying Ding
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunzhou road, Kunming, 650118, China
| | - Ke Zhao
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, China
| | - Chu Han
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, China
| | - Zhenwei Shi
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, China
| | - Yanfen Cui
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, China
| | - Chunling Liu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Huan Lin
- School of Medicine, South China University of Technology, Guangzhou, 510006, China
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China
| | - Xipeng Pan
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China
- Guangdong Cardiovascular Institute, Guangzhou, 510080, China
| | - Pinxiong Li
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, 510515, China
| | - Minglei Chen
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China
- Shantou University Medical College, Shantou, 515063, China
| | - Huihui Wang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China
- Shantou University Medical College, Shantou, 515063, China
| | - Xiaohui Deng
- Department of Information Management, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Changhong Liang
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China
| | - Yu Xie
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunzhou road, Kunming, 650118, China
| | - Zaiyi Liu
- School of Medicine, South China University of Technology, Guangzhou, 510006, China
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China
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Non-invasive predictors of axillary lymph node burden in breast cancer: a single-institution retrospective analysis. Breast Cancer Res Treat 2022; 195:161-169. [PMID: 35864309 PMCID: PMC9374610 DOI: 10.1007/s10549-022-06672-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 07/02/2022] [Indexed: 11/13/2022]
Abstract
Purpose Axillary staging is an important prognostic factor in breast cancer. Sentinel lymph node biopsy (SNB) is currently used to stage patients who are clinically and radiologically node-negative. Since the establishment that axillary node clearance (ANC) does not improve overall survival in breast-conserving surgery for patients with low-risk biological cancers, axillary management has become increasingly conservative. This study aims to identify and assess the clinical predictive value of variables that could play a role in the quantification of axillary burden, including the accuracy of quantifying abnormal axillary nodes on ultrasound. Methods A retrospective analysis was conducted of hospital data for female breast cancer patients receiving an ANC at our centre between January 2018 and January 2020. The reference standard for axillary burden was surgical histology following SNB and ANC, allowing categorisation of the patients under ‘low axillary burden’ (2 or fewer pathological macrometastases) or ‘high axillary burden’ (> 2). After exploratory univariate analysis, multivariate logistic regression was conducted to determine relationships between the outcome category and candidate predictor variables: patient age at diagnosis, tumour focality, tumour size on ultrasound and number of abnormal lymph nodes on axillary ultrasound. Results One hundred and thirty-five patients were included in the analysis. Logistic regression showed that the number of abnormal lymph nodes on axillary ultrasound was the strongest predictor of axillary burden and statistically significant (P = 0.044), with a sensitivity of 66.7% and specificity of 86.8% (P = 0.011). Conclusion Identifying the number of abnormal lymph nodes on preoperative ultrasound can help to quantify axillary nodal burden and identify patients with high axillary burden, and should be documented as standard in axillary ultrasound reports of patients with breast cancer.
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Cocco D, Valente SA. ASO Author Reflection: Updating our Knowledge on Invasive Lobular Breast Cancer. Ann Surg Oncol 2021; 29:545-546. [PMID: 34370144 DOI: 10.1245/s10434-021-10615-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 07/06/2021] [Indexed: 11/18/2022]
Affiliation(s)
- Daniela Cocco
- Division of Breast Surgery, Department of General Surgery, Cleveland Clinic, Cleveland, OH, USA
| | - Stephanie A Valente
- Division of Breast Surgery, Department of General Surgery, Cleveland Clinic, Cleveland, OH, USA.
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Cocco D, ElSherif A, Wright MD, Dempster MS, Kruse ML, Li H, Valente SA. Invasive Lobular Breast Cancer: Data to Support Surgical Decision Making. Ann Surg Oncol 2021; 28:5723-5729. [PMID: 34324111 DOI: 10.1245/s10434-021-10455-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 06/22/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND Invasive lobular carcinoma (ILC) is thought be a unique entity with higher rates of multifocal/multicentric and bilateral disease. This study aimed to evaluate the true extent of the disease, risk of bilaterality, lymph node involvement, and impact of preoperative imaging to help guide surgical decision making. METHODS A retrospective analysis identified patients treated for ILC between 2004 and 2017. Clinical staging and pathologic results were compared. Follow-up details including local recurrence, contralateral breast cancer (CBC), and survival outcomes were evaluated. RESULTS The study identified 692 patients with ILC, including 43 patients (6%) with a diagnosis of CBC and 232 patients (33%) with a diagnosis of multifocal/multicentric disease at presentation. Preoperative magnetic resonance imaging (MRI) led to an identification of additional disease in 20% of the patients. Preoperative MRI resulted in a more accurate prediction of tumor size staging but did not improve the discordance between clinical and pathologic nodal staging. Overall, the rate of imaging occult lymph node disease was 24%. At the 6-year follow-up evaluation, a local recurrence had developed in 2.3%, a CBC in 2.3, and a distant metastasis in 9.4% of the patients. The overall survival rate was 96% at 3 years and 91% at 5 years. CONCLUSIONS Invasive lobular carcinoma is a distinct subset of cancer that poses a diagnostic staging challenge. The results of this study favor MRI for accurate tumor staging and for improving detection of multicentricity and bilaterality. However, clinicians should be aware of the higher likelihood of occult lymph node involvement with ILC and subsequent early metastasis.
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Affiliation(s)
- Daniela Cocco
- Division of Breast Surgery, Department of General Surgery, Cleveland Clinic, Cleveland, OH, USA
| | - Ayat ElSherif
- Division of Breast Surgery, Department of General Surgery, Cleveland Clinic, Cleveland, OH, USA
| | - Matthew D Wright
- Division of Breast Medical Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Marcus S Dempster
- Division of Breast Medical Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Megan L Kruse
- Division of Breast Medical Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Hong Li
- Department of Quantitative Health Science, Cleveland Clinic, Cleveland, OH, USA
| | - Stephanie A Valente
- Division of Breast Surgery, Department of General Surgery, Cleveland Clinic, Cleveland, OH, USA.
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